MUSA: A Multimodal MRI-Based Major Depressive Disorder Classification Model with a Unified ROI Semantic Space

27/10/2026

AI Summary

Proposes MUSA: an AAL-guided unified ROI semantic space to align heterogeneous multimodal MRI features across consistent brain-region correspondence.
Implements ROI-level 3D Vision Transformer to encode voxelwise ROI information into fixed-dimensional embeddings integrating structural, local functional, and connectivity data.
Validated on 2380 REST-meta-MDD participants; WM+FC+ReHo+DC achieved 79.20% ±0.65% accuracy and 87.08% ±0.51% AUC; discriminative regions identified.

TL;DR (AI Generated)

However, existing multimodal MRI fusion models often use data-level fusion or post hoc feature concatenation, potentially overlooking differences in spatial resolution, voxel organization, and region of interest (ROI)-level semantic correspondence.

To address this issue, this study proposes MUSA, a unified ROI semantic space model for multimodal MRI-based MDD classification.

An ROI-level 3D Vision Transformer (3D ViT) encodes ROI voxel information into fixed-dimensional representations, allowing structural, local functional, and connectivity information to be organized and integrated within a consistent brain-region semantic space.

Basic summary

Sensors (Basel). 2026 Oct 15;26(20):6555. doi: 10.3390/s26206555.

ABSTRACT

Major depressive disorder (MDD) is a common disabling psychiatric disorder mainly diagnosed through clinical interviews and rating-scale assessments. Magnetic resonance imaging (MRI) provides a promising basis for computer-aided MDD identification, particularly through multimodal models that can integrate structural and functional brain information. However, existing multimodal MRI fusion models often use data-level fusion or post hoc feature concatenation, potentially overlooking differences in spatial resolution, voxel organization, and region of interest (ROI)-level semantic correspondence. To address this issue, this study proposes MUSA, a unified ROI semantic space model for multimodal MRI-based MDD classification. An Automated Anatomical Labeling (AAL) atlas-guided ROI semantic space establishes consistent brain-region correspondence across heterogeneous MRI features. An ROI-level 3D Vision Transformer (3D ViT) encodes ROI voxel information into fixed-dimensional representations, allowing structural, local functional, and connectivity information to be organized and integrated within a consistent brain-region semantic space. Experiments on 2380 participants from REST-meta-MDD validated MUSA for MDD and normal controls (NCs) classification. The feature combination of white matter (WM), functional connectivity (FC), regional homogeneity (ReHo), and degree centrality (DC) achieved 79.20% ± 0.65% accuracy and an AUC of 87.08% ± 0.51%. Brain-region importance analysis identified classifier-derived discriminative patterns across frontoparietal, sensorimotor, visual, temporal, and limbic-related regions.

PMID:42944418 | DOI:10.3390/s26206555

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